Brian Gilbert,Elizabeth L Ogburn,Abhirup Datta
Brian Gilbert
This article addresses the asymptotic performance of popular spatial regression estimators of the linear effect of an exposure on an outcome under spatial confounding, the presence of an unmeasured spatially structured variable influencing ...
Integer programming for learning directed acyclic graphs from nonidentifiable Gaussian models [0.03%]
整数规划在从非可识别高斯模型中学习有向无环图中的应用
Tong Xu,Armeen Taeb,Simge Küçükyavuz et al.
Tong Xu et al.
We study the problem of learning directed acyclic graphs from continuous observational data, generated according to a linear Gaussian structural equation model. State-of-the-art structure learning methods for this setting have at least one ...
Sensitivity models and bounds under sequential unmeasured confounding in longitudinal studies [0.03%]
具有一系列未测量混淆变量的纵向研究中的敏感性模型及界值分析方法
Zhiqiang Tan
Zhiqiang Tan
We consider sensitivity analysis for causal inference in a longitudinal study with time-varying treatments and covariates. It is of interest to assess the worst-case possible values of counterfactual outcome means and average treatment effe...
K E Rudolph,N T Williams,E A Stuart et al.
K E Rudolph et al.
We develop flexible, semiparametric estimators of the average treatment effect (ATE) transported to a new target population that offer potential efficiency gains. Transport may be of value when the ATE may differ across populations. We cons...
Yinxiang Wu,Hyunseung Kang,Ting Ye
Yinxiang Wu
Multivariable Mendelian randomization (MVMR) uses genetic variants as instrumental variables to infer the direct effects of multiple exposures on an outcome. However, unlike univariable Mendelian randomization, MVMR often faces greater chal...
With random regressors, least squares inference is robust to correlated errors with unknown correlation structure [0.03%]
带有随机解释变量时最小二乘法具有稳健性——无需知道误差相关性的具体形式就能应对任意相关的扰动项
Zifeng Zhang,Peng Ding,Wen Zhou et al.
Zifeng Zhang et al.
Linear regression is arguably the most widely used statistical method. With fixed regressors and correlated errors, the conventional wisdom is to modify the variance-covariance estimator to accommodate the known correlation structure of the...
A general form of covariate adjustment in clinical trials under covariate-adaptive randomization [0.03%]
协变量调整适应性随机化临床试验的疗效评价方法研究
Marlena S Bannick,Jun Shao,Jingyi Liu et al.
Marlena S Bannick et al.
In randomized clinical trials, adjusting for baseline covariates can improve credibility and efficiency for demonstrating and quantifying treatment effects. This article studies the augmented inverse propensity weighted estimator, which is ...
D Agnoletto,T Rigon,D B Dunson
D Agnoletto
Generalized linear models are routinely used for modelling relationships between a response variable and a set of covariates. The simple form of a generalized linear model comes with easy interpretability, but also leads to concerns about m...
Richard A Davis,Leon Fernandes
Richard A Davis
A fundamental and often final step in time series modelling is to assess the quality of fit of a proposed model to the data. Since the underlying distribution of the innovations that generate a model is often not prescribed, goodness-of-fit...
Radial Neighbors for Provably Accurate Scalable Approximations of Gaussian Processes [0.03%]
用于准确可扩展高斯过程逼近的径向邻居
Yichen Zhu,Michele Peruzzi,Cheng Li et al.
Yichen Zhu et al.
In geostatistical problems with massive sample size, Gaussian processes can be approximated using sparse directed acyclic graphs to achieve scalable O ( n ) computational complexity. In these models, data at each location are typically assu...